Electronics and information technologies / Електроніка та інформаційні технології
ISSN 2224-087Х (Print) ISSN 2224-0888 (Online) ISSN Center responsible for the record: CIEPS — ISSN Digital identifier DOI: https://doi.org/10.30970/eli Certificate of state registration of the print media: Decision of the National Council of Ukraine on Television and Radio Broadcasting No. 1877 dated May 30, 2024. Media identifier R30-04912. Language of publication: English or Ukrainian, with Abstracts in both Ukrainian and English. The collection of scientific papers "Electronics and Information Technologies" has been published since 1966. In 1966-2010, the journal was published under the title "Theoretical Electrical Engineering". "Theoretical Electrical Engineering" is one of the first scientific and technical publications in Ukraine devoted to the problems of theoretical electrical engineering, electronics, and modeling. The collection was created on the initiative of the rector of Lviv State University named after I. Franko, Professor M. Maksymovych, who was the editor-in-chief of the collection until 1981. Subsequently, the chief editors of the collection were: Professor L. Synytsky (1981-1991), Professor P. Stakhiv (1991-1996), and Professor I. Bolesta (1996-2024). Since 2025, the annual collection has been headed by chief co-editors Professors Oleh Bordun and Ivan Karbovnyk. During all the years of its existence, the journal was included in the list of publications approved by the Higher Attestation Commission, in which the main results of dissertations may be published. At present, the journal is included in the List of Scientific Professional Publications of Ukraine (Category “B”) in the “Information Technologies and Electronics” cluster in the following specialties: F2 Software Engineering; F3 Computer Science; F4 Systems Analysis and Data Science; F5 Cybersecurity and Information Protection; F6 Information Systems and Technologies; F7 Computer Engineering; G5 Electronics, Electronic Communications, Instrumentation and Radio Engineering; G6 Information and Measurement Technologies; G7 Automation, Computer-Integrated Technologies, and Robotics. All articles in the collection are assigned DOI digital identifiers from the official representative of CrossRef in Ukraine. Founder & Publisher: Ivan Franko National University of Lviv, Ukraine (EDRPOU Code: 02070987) Frequency of Publishing: Quarterly* *In some cases, one joint issue of the collection may be published at the end of the year. In that case, the articles will appear for the preview on the journal’s website as soon as they have been accepted by the editorial board. Publication Fee: Publication in the collection Електроніка та інформаційні технології / Electronics and Information Technologies is free of charge! Open Access Policy: Електроніка та інформаційні технології / Electronics and information technologies adopts a platinum open access policy to published content in accordance with the principle of free dissemination of scientific information and global exchange of knowledge for common civil progress (http://www.budapestopenaccessinitiative.org and Creative Commons Attribution 4.0 license https://creativecommons.org/licenses/by/4.0). Archiving: the full archive of the collection Електроніка та інформаційні технології / Electronics and information technologies is freely accessible without any restrictions in the National Library of Ukraine named after V. I. Vernadskyi and LOCKSS. The online archive is posted on the portal of the Ivan Franko National University of Lviv. We accept manuscript submissions for Issue 34, scheduled for publication in June 2026. Please, pay attention to the changes in the requirements for Abstracts, Materials and Methods, and References. Template Sample
We ask authors to sign an open-access license agreement. All articles are distributed under the Creative Commons Attribution (CC-BY) license (https://creativecommons.org/licenses/by/4.0), which means that readers of the collection Electronics and Information Technologies have the right to use, reproduce, publish, and distribute these articles, provided that: 1) citation with the correct indication of the authorship of the article; 2) correct entry of data on the publication and place of publishing; 3) If the original work is reproduced or distributed in part, this should be clearly indicated. Authors retain copyright and distribution rights to their works. They have the right to post and store all versions of their articles in institutional or industry repositories, including: ● preprint ● The author's manuscript was accepted by the editorial board ● the published article. The author's declaration must be signed by all authors (send a scanned version with signatures to the editorial office by e-mail).
USING LARGE LANGUAGE MODELS FOR TEXT ANALYSIS IN THE EVALUATION OF UNIVERSITY EDUCATIONAL PROGRAMS
Background. Large language models (LLMs) are increasingly used in educational analytics, particularly for processing large volumes of accreditation-related documents. However, it remains unclear how reliably such models can assess the quality of self-evaluation reports for educational programs, and which textual characteristics influence how models form their assessments. Materials and Methods. In the study, ten self-evaluation reports of educational programs were analyzed: five identified by the expert assessment as the strongest within the higher education institution over the last three years, and five as the weakest over the same period. GPT-5 and Gemini-2.5 models independently evaluated each document using the official ten Ukrainian National Agency for Higher Education Quality Assurance (NAQA) criteria and eight textual metrics reflecting structural, semantic, argumentative, and factual properties of the text. All evaluation grades were generated directly by the models on a unified scale from 1 to 10. To analyze the relationships between NAQA and textual criteria, Pearson's and Spearman’s correlation coefficients were used. Results and Discussion. LLMs demonstrated limited alignment with the NAQA criteria, yielding weak correlations. In contrast, textual criteria, primarily factual density, argumentativeness, semantic coherence, and lexical diversity, consistently differentiated between stronger and weaker reports. GPT-5 exhibited lower variability and reduced sensitivity to stylistic noise, while Gemini-2.5 reacted more strongly to structural and stylistic deficiencies. Correlation matrices showed that textual criteria better capture the latent quality characteristics of documents than the direct application of NAQA criteria. Conclusion. The results show that LLMs currently do not accurately reproduce expert evaluations based on the formal NAQA criteria but effectively analyze the structural and content-related characteristics of reports using textual metrics. These metrics complement the NAQA criteria by accelerating expert workflows and enhancing document monitoring. Future research will focus on expanding the dataset, standardizing prompts, and comparing a broader range of models. Keywords: large language models, educational programs, quality assessment.
Copyright (c) 2026 Bohdan Pavlyshenko, Mykola Stasiuk, Vitaliy Kukharskyy http://creativecommons.org/licenses/by/4.0
PHASED INTEGRATION OF NEURAL NETWORKS OF DIFFERENT ARCHITECTURES IN MATHEMATICAL COMPUTING SYSTEMS
Background. The development of modern mathematical computing systems requires the effective implementation of machine learning algorithms while maintaining a balance between prediction accuracy and computational resources. Particular attention should be given to the phased integration of neural networks of varying complexity with minimized risks for production systems and investigation of the saturation effect when increasing architectural depth. Materials and Methods. This article aims to develop a methodology for evolutionary integration of neural networks from simple perceptrons to ultra-deep architectures in mathematical computing systems, with detailed comparative analysis of four architectural types and mathematical modeling of the accuracy saturation effect. Results and Discussion. For this purpose, four neural network architectures were investigated: a single-layer perceptron, a four-layer network (128→64→32), a ten-layer network (128→96→64→48→32→24→16→12), and a twenty-layer architecture with gradual dimensionality reduction. Experiments were conducted on a dataset from mathematical modeling results containing 45,000 samples with 24 characteristics. A comprehensive system of metrics was used to evaluate accuracy, processing speed, resource consumption, and model stability. The experimental design included stratified data splitting and cross-validation to ensure statistical reliability of the obtained results across different architectural configurations. Conclusion. As a result, the single-layer perceptron demonstrated baseline accuracy of 78.3% with minimal resource consumption (45 MB RAM, 15 ms latency). The four-layer network achieved 94.1% accuracy with a moderate increase in resource costs. The ten-layer architecture showed 95.6% accuracy, demonstrating the beginning of the saturation effect. The twenty-layer network achieved only 96.8% accuracy with disproportionate growth in resource consumption (1024 MB RAM, 270 ms latency). Mathematical modeling confirmed the logistic nature of the relationship between accuracy and architectural complexity. The findings provide practical guidelines for selecting optimal neural network architectures in resource-constrained production environments, establishing clear thresholds beyond which increased complexity yields diminishing returns. Keywords: Neural networks, evolutionary integration, mathematical computing, deep learning, saturation effect, architecture optimization
Copyright (c) 2026 Mykhailo Bavdys, Oleksii Kushnir http://creativecommons.org/licenses/by/4.0
INDOOR POSITIONING WITH BLUETOOTH LOW ENERGY AND EXTENDED KALMAN FILTER
Background. Indoor positioning systems based on Bluetooth Low Energy (BLE) beacons widely rely on estimating distance using the received signal strength indicator (RSSI). However, RSSI measurements in indoor environments are significantly affected by multipath propagation, shadowing, interference, and absorption by obstacles, resulting in high variability of signal strength and substantial distance estimation errors. The nonlinear logarithmic relationship between RSSI and distance further complicates the application of conventional linear filtering techniques such as the classical Kalman Filter, which requires prior transformation of measurements and may lead to loss of optimality. Materials and Methods. This study proposes a distance estimation method based on the Extended Kalman Filter (EKF), which directly processes RSSI measurements using the nonlinear log-distance path loss model. The experiment was performed in an indoor office environment using two Silicon Labs EFR32BG22 BLE beacons and a Nordic nRF52840 receiver. The EKF parameters were selected based on prior calibration of the propagation model coefficients. Results and Discussion. The experimental results demonstrate that the EKF effectively smooths RSSI. For the beacon with lower RSSI dispersion, the root mean square error (RMSE) reached 0.14 m, for the second beacon, the RMSE was 0.53 m. The analysis confirms that estimation accuracy strongly depends on signal stability and calibration quality. Compared to direct RSSI-to-distance conversion and the classical Kalman Filter approach reported in related work, the EKF-based algorithm reduces the mean absolute distance estimation error by approximately 20–30%, validating the advantages of nonlinear filtering. Conclusion. The proposed EKF-based method improves the accuracy and robustness of RSSI-based distance estimation in BLE indoor positioning systems. When model parameters are properly calibrated, the achieved accuracy is sufficient for practical applications such as smart building navigation, asset tracking, and robotic localization. The algorithm can be implemented on resource-constrained embedded platforms and serves as a foundation for further development of multisensor indoor positioning systems. Keywords: positioning, Bluetooth, BLE, RSSI, Kalman filter, Extended Kalman Filter
Copyright (c) 2026 Tadei-Nazarii Kalynchuk http://creativecommons.org/licenses/by/4.0
COMMUNICATION ARCHITECTURES FOR CLOUD-INTEGRATED SPECTROMETRIC LABORATORIES: ESP-NOW VS MQTT
Background. Cloud-integrated spectrometric laboratories face communication challenges in achieving real-time data access and analysis. This study compares two wireless protocols, MQTT (Message Queuing Telemetry Transport) and ESP-NOW (Espressif NOW), for LED control in such environments. MQTT offers lightweight, bandwidth-efficient, publish-subscribe messaging [1–3], while ESP-NOW provides energy-efficient direct communication without a Wi-Fi router. The objective is to evaluate their performance and suitability. Materials and Methods. An experimental setup involved a StellarNet spectrometer, LED light sources, and ESP32 microcontrollers. Two architectures were tested: 1) direct MQTT Communication, where each ESP32 connected directly via Wi-Fi to an MQTT broker; and 2) an edge device with ESP-NOW relay, using an edge ESP32 for MQTT/Wi-Fi communication, then relaying commands via ESP-NOW to other ESP32s. Response times for LED control were measured over 100 cycles, and data were analyzed using descriptive statistics and an independent samples t-test. Results and Discussion. Direct MQTT Communication exhibited significantly lower latency (median ~60 ms) and tighter distribution compared to the Edge Device with ESP-NOW Relay (median ~170 ms). A t-test confirmed a statistically significant difference (t=-46.28), with MQTT demonstrating faster response times. However, the ESP-NOW relay system offers architectural advantages: reduced Wi-Fi dependency for individual nodes, enhanced deployment flexibility in areas with poor Wi-Fi coverage, improved scalability [4], and energy efficiency, making its higher latency a practical trade-off for large-scale laboratory integration. Conclusion. Direct MQTT Communication provides superior low-latency performance. However, the edge device with ESP-NOW relay, despite higher latency, is a highly acceptable solution due to its flexibility, scalability, and reduced Wi-Fi dependency for distributed spectrometric laboratories. This highlights a critical trade-off between absolute speed and architectural benefits for robust, smart, cloud-enabled analytical laboratories. Keywords: MQTT, ESP-NOW, spectrometric laboratory, Internet of Things, latency, embedded systems hardware
Copyright (c) 2026 Andriy Krupych, Pavlo Levush http://creativecommons.org/licenses/by/4.0
EVALUATION OF THE PERFORMANCE OF A MULTI-LEVEL MODEL FOR ANOMALOUS DNS QUERY DETECTION
Background. In modern network security systems, DNS (Domain Name System) traffic has become an increasingly attractive vector for covert data exfiltration and command-and-control communication. Existing machine learning methods frequently suffer from limited adaptability to novel attack patterns and an imbalance between detection accuracy and false positive rates. Materials and Methods. This study proposes TunnelEye, a multi-level detection method for malicious DNS queries that integrates statistical feature analysis, structural n-gram modeling, and anomaly detection. Statistical properties of domain names, including string length, entropy, and alphanumeric ratio, are used for initial discrimination between benign and suspicious queries. Structural analysis based on character n-grams enables the identification of local patterns associated with encoded data such as Base32 and Base64. An autoencoder trained exclusively on legitimate DNS queries is employed as an independent anomaly detector to identify previously unseen and zero-day attacks. The supervised TunnelEye classifier and the autoencoder operate in parallel, each using an independently optimized F1-score based threshold to determine anomalous DNS queries. Results and Discussion. Experimental evaluation using standard machine learning metrics (precision, recall, F1-score, ROC-AUC, PR-AUC, and false positive rate) demonstrates that TunnelEye consistently outperforms baseline statistical models and standalone autoencoders. The proposed method achieves high precision and recall while maintaining a minimal false positive rate. Experimental results show that TunnelEye achieves an average precision, recall, and F1-score of approximately 0.99, outperforming the baseline statistical model by more than 10% and significantly reducing the false positive rate. Conclusion. TunnelEye provides a comprehensive and adaptive solution for malicious DNS query detection by combining supervised and unsupervised learning with dynamic threshold optimization. Its ability to balance detection accuracy and false positive reduction makes it well suited for deployment in modern enterprise cybersecurity systems for real-time DNS traffic monitoring. Keywords: DNS traffic, anomaly detection, machine learning, multi-level model.
Copyright (c) 2026 Andrii Senyk http://creativecommons.org/licenses/by/4.0
APPLICATION OF PENETRATION TESTING FOR ASSESSING THE INFORMATION SECURITY LEVEL OF WEB-ORIENTED INFORMATION SYSTEMS
Background. The increasing role of web-oriented information systems in business, education, and public administration is accompanied by a growing number and complexity of cyber threats. Traditional security mechanisms do not always enable the identification of actual system weaknesses, which necessitates the application of practice-oriented methods for assessing the level of information security. In this context, Penetration Testing is considered an effective instrument for simulating the actions of a potential attacker in order to detect and validate exploitable vulnerabilities. Materials and Methods. The study employs a risk-oriented approach in accordance with international standards ISO/IEC 27001 and ISO/IEC 27005, as well as the recom­mendations of OWASP and NIST SP 800-115. Penetration Testing is implemented as a structured, multi-stage process that includes information gathering, attack surface analy­sis, threat modeling, execution of non-invasive validation scenarios, and risk assessment. The practical component was conducted in a controlled test environment using Nmap, Burp Suite, and Wireshark, supplemented by custom-developed Python modules for automated analysis of HTTP security headers, TLS certificates, and exposed services. Results and Discussion. The study identified several configuration-related weaknesses at the application level, including the absence of essential HTTP security headers and deficiencies in TLS certificate management. The obtained results were formalized in a structured findings register with quantitative risk evaluation based on the Likelihood × Impact model. The analysis demonstrated that even in the absence of critical exploitable vulnerabilities, configuration errors significantly increase the overall risk level and may create preconditions for more sophisticated attacks. Conclusion. The findings confirm the effectiveness of Penetration Testing as a comprehensive instrument for assessing the information security of web-oriented systems. The proposed approach facilitates the transition from technical testing results to substantiated managerial decisions aimed at risk reduction and enhancement of the overall protection level of information resources. Keywords: information security, penetration testing, vulnerabilities, risk assessment, web-oriented information systems.
Copyright (c) 2026 Serhiy Sveleba, Ivan Katerynchuk, Ivan Kunyo, Oleh Krupych, Yaroslav Shmyhelskyy, Marta Dufanets, Natalia Sveleba, Lucjan Pelc, Volodymyr Brygilevych http://creativecommons.org/licenses/by/4.0
LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK FOR STATE PREDICTION AND RESOURCE ALLOCATION OPTIMIZATION IN DISTRIBUTED SYSTEMS
Introduction. This paper considers a method based on a Long Short-Term Memory (LSTM) neural network for optimal resource allocation in distributed systems. The developed algorithm ensures high accuracy in predicting resource states and optimal spatial distribution with minimal processing time. The relevance of this research is determined by the growing need for intelligent automation of resource management processes in service infrastructure facilities. A locker management system in sports facilities is used as a practical demonstration of the method's effectiveness. Materials and Methods. To address the prediction and optimization tasks, an LSTM-based architecture with 32 hidden neurons and a sequence length of 10 time steps is proposed. The LSTM model processes sequential occupancy data to capture temporal dependencies and generate probability estimates for future resource states. A multi-factor scoring function is developed to transform predictions into optimal allocation decisions, considering spatial constraints and user preferences. The method is systematically compared with classical approaches: heuristic algorithms (Sequential, Round-Robin), statistical time series models (ARIMA, exponential smoothing), and machine learning methods (logistic regression, random forest, gradient boosting). All methods are evaluated on identical datasets using consistent metrics, including prediction accuracy, F1-score, spatial balance index, and zone variance. Results. Using LSTM neural networks for the prediction task achieves 85% accuracy, which is statistically significantly higher than Random Forest (79%, p=0.0023) and ARIMA (68%, p=0.0001). The spatial balance index improved by 8.5% compared to the best classical method (0.89 versus 0.82). Inference time remains acceptable for real-time applications (18.9 ms per prediction). Conclusions. The proposed LSTM-based method demonstrates satisfactory accuracy in predicting resource states and optimizing their allocation within minimal timeframes. The ability to model long-term temporal dependencies provides significant advantages over classical fixed-window methods. Therefore, the method can be effectively applied to enhance the functionality of distributed resource management systems. Keywords: LSTM neural network, resource allocation, time series prediction, recurrent neural networks, optimization.
Copyright (c) 2026 Zinovii Liubun, Oleh Tereshchuk http://creativecommons.org/licenses/by/4.0
INTELLIGENT ANALYSIS OF PERFORMANCE RESULTS BASED ON OBJECT-RELATIONAL MAPPING STRATEGIES AND FOREIGN KEY CONSTRAINTS IN SQL DATABASES
Background. The rapid expansion of data-driven applications has increased the importance of efficient query execution in relational database systems, where even minor inefficiencies can significantly affect overall performance. Although Object-Relational Mapping (ORM) frameworks simplify development and improve maintainability, their abstraction layer can introduce measurable overhead, and the impact of foreign key constraints on execution speed remains a practical concern, particularly in microservice architectures that follow the “Database per Service” principle. Materials and Methods. An experimental information system is developed using a relational database and the SQLAlchemy ORM framework, with a schema that includes one-to-one, one-to-many, and many-to-many relationships tested both with and without foreign key constraints. Three representative queries retrieving booking details, aggregating related records, and calculating total payments are executed using raw SQL and ORM approaches, while an intelligent algorithm analyzed performance, detected potential N+1 query risks, and recommended optimal strategies such as explicit JOINs. Results and Discussion. Raw SQL consistently demonstrated superior performance across all scenarios. The most significant disparity occurred in ORM implementations affected by the N+1 problem, where execution time exceeded that of equivalent SQL queries by more than an order of magnitude. Aggregation queries showed smaller yet consistent overhead. The presence or absence of foreign key constraints had a negligible influence on raw SQL performance, with differences remaining within experimental variance. Explicit JOIN usage in ORM substantially reduced overhead compared to implicit relationship navigation. The intelligent analysis accurately predicted high-risk queries and provided effective strategy recommendations, confirmed by empirical results. Conclusion. ORM frameworks improve productivity and maintainability but introduce measurable overhead. Raw SQL remains preferable for performance-critical tasks, while foreign key constraints do not significantly degrade execution speed. Intelligent performance analysis supports balanced decisions between efficiency and maintainability in complex relational systems. Keywords: relational databases, SQL performance, ORM, decision support system, intelligent analysis, database design
Copyright (c) 2026 Roman Mysiuk http://creativecommons.org/licenses/by/4.0
SPATIAL-GEOMETRIC EVALUATION OF LOCAL FEATURES IN MONOCULAR VISUAL ODOMETRY
Background. Monocular visual odometry is an important component of visual navigation systems. However, its accuracy depends on the quality of local features and inter-frame correspondences. In the VO task, not only is geometric consistency important, but also motion observability, the physical validity of the recovered configuration, and the spatial-structural properties of local features. This study aims to provide a comprehensive evaluation of keypoint detection and description methods for monocular visual odometry. Materials and Methods. The study was conducted on the EuRoC MAV dataset. The ORB, BRISK, AKAZE, KAZE, SIFT, SURF, and SuperPoint methods were analyzed for the number of keypoints, ranging from 200 to 1000. Motion estimation was performed using the essential matrix, the USAC_FAST filter, the recoverPose method, a minimum parallax check, and spatially guided keypoint selection. The accuracy of the recovered trajectory was evaluated using the APE and RPE metrics. To analyze the quality of local features and correspondences, the geometric component, the parallax indicator, the correct cheirality ratio, and metrics of keypoint coverage uniformity, local redundancy, and structural consistency were used. An integral quality indicator was applied to summarize the results. Results and Discussion. The geometric metrics most often highlight AKAZE and SURF, whereas SuperPoint shows strong performance in terms of spatial characteristics. In terms of the structural consistency of correspondences, SURF consistently demonstrates the best results. As the number of keypoints increases, most methods show an initial improvement followed by saturation, and in some cases, a deterioration of individual characteristics. SURF was found to be the most balanced method across the set of criteria, whereas ORB showed the weakest results in most cases. The correlation analysis showed that the informativeness of the metrics varies by sequence type. Conclusion. The proposed approach confirmed the relevance of multicriteria evaluation of local features in monocular visual odometry. It was shown that no single metric is universal across all scene types. In contrast, the integral indicator enables the summary of different aspects of quality and a more well-grounded ranking of the methods. Keywords: monocular visual odometry, keypoint detection, image matching, motion estimation, deep learning, neural networks.
Copyright (c) 2026 Andriy Fesiuk, Yuriy Furgala http://creativecommons.org/licenses/by/4.0
ENTROPY-GUIDED TRACKER SWITCHING METHOD FOR UNMANNED AERIAL VEHICLE REAL-TIME TRACKING
Background. Auto-guidance for unmanned aerial vehicles (UAVs) requires reliable real-time target tracking on resource-constrained onboard hardware. Modern state-of-the-art CNN-based and Transformer-based deep trackers provide strong accuracy but are often too slow and computationally expensive for continuous deployment on edge devices. In contrast, lightweight correlation-filter trackers run at high frame rates but can easily drift or lose the target because of occlusions or fast maneuvers. This robustness–efficiency trade-off (edge AI paradox) motivates adaptive strategies that balance accuracy, speed, and resource usage while preserving compute headroom for other onboard tasks. Materials and methods. We propose an entropy-guided tracker switching method that combines a lightweight kernelized correlation filter (KCF) tracker augmented with Kalman motion prediction and a more accurate Siamese deep tracker. A motion-entropy scheduler quantifies the unpredictability of target motion using a normalized Shannon entropy over recent orientation changes. To avoid reacting to transient spikes, the entropy is exponentially smoothed, and threshold rules (with hysteresis) determine when KCF is sufficient and when to activate the deep tracker. Results and Discussion. Experiments on UAV benchmarks (UAV123, OTB100) show that the hybrid tracker improves success AUC by ~10% over KCF and reaches about 70% of a Transformer tracker’s AUC while running 1.5–3× faster than always-on deep tracking. The switcher invokes the deep tracker only during difficult intervals, sustaining real-time operation (~100 FPS) and reducing average computation to ≈0.6 GFLOPs per frame versus ≈1–4 GFLOPs for purely deep tracking. Conclusion. The proposed motion-entropy scheduler enables an adaptive trade-off between efficiency, speed, and accuracy. It maintains high tracking precision during target maneuvers and occlusions by temporarily switching to a robust tracker yet saves computational load during steady-motion periods. This framework offers a practical solution for high-performance UAV tracking on the edge, while leaving resource headroom to apply other improvement techniques. Keywords: object tracking; correlation filters; Siamese network; motion entropy; hybrid tracker; edge computing.
Copyright (c) 2026 Volodymyr Oleksiuk, Sergiy Velgosh http://creativecommons.org/licenses/by/4.0
PREDICTIVE THERMAL MANAGEMENT IN EMBEDDED ELECTRONICS USING DEEP REINFORCEMENT LEARNING
Background. This paper presents a deep reinforcement learning approach for intelligent thermal management in embedded electronics, targeting energy-efficient and safe operation under dynamic workloads. A custom hardware switching circuit based on an NPN transistor was designed to enable GPIO-driven fan actuation on a resource-constrained platform. Materials and Methods. A real-time dataset was collected from a Raspberry Pi Zero W, capturing CPU temperature, usage metrics, and fan states over a 12-hour controlled experiment. The thermal regulation task was modeled as a Markov Decision Process, and a Deep Q-Network (DQN) was trained to learn optimal fan activation policies. The trained model was deployed directly on-device, interfaced with a custom GPIO-controlled fan circuit. Inference was performed in less than one millisecond per decision step using a lightweight PyTorch runtime. Results and Discussion. Evaluation results show that the DQN policy reduced total fan activation time by 23.2% compared to the rule-based hysteresis baseline, while maintaining CPU temperature below 60°C for over 99% of the test duration. The trained agent activated the fan only 23.7% of the time, demonstrating a conservative and energy-aware cooling strategy. Confusion matrix analysis yielded a precision of 1.000, a recall of 1.000, and an F1-score of 1.000 across 3442 model-controlled evaluation steps. The model correctly identified all 22 fan activation events without any false positives or false negatives. Comparative analysis against nine recent AI-driven approaches showed that the proposed method achieved an 11.2°C temperature reduction and 36.5% energy savings, while operating entirely on-device without cloud dependence. Conclusion. The model exhibited stable reward convergence, accurate action prediction, and anticipatory control that minimized overheating events. Thermal traces confirmed smooth transitions and low variance, demonstrating the feasibility of deploying learning-based thermal policies in real-time edge environments. This work contributes a practical framework for energy-aware cooling and provides a pathway for adaptive thermal intelligence in low-resource embedded systems. Keywords: thermal management, deep reinforcement learning, embedded systems, Deep Q-Network, energy-efficient cooling, real-time inference.
Copyright (c) 2026 Oleh Yatskiv, Bohdan Koman http://creativecommons.org/licenses/by/4.0
VISION TRANSFORMER-BASED FALL DETECTION: A SPATIAL TEMPORAL ATTENTION MECHANISM FOR ROBUST VIDEO ANALYSIS
Background. Fall detection is a critical challenge in healthcare and elderly care, as delayed response often leads to severe injuries. With ageing populations, fall-related admissions continue to rise, increasing demands on automated monitoring. Approaches based on wearable devices or conventional classifiers produce frequent false alarms and show limited adaptability. Video-based systems offer broader coverage but still require models that capture posture and motion changes without handcrafted features. Vision Transformers, originally developed for image recognition, provide a promising alternative by leveraging self-attention to model complex dependencies across spatial and temporal dimensions. Materials and Methods. A Vision Transformer framework was applied to model spatial and temporal patterns in human motion. Video frames were divided into patches and projected into token embeddings, with multi-head self-attention tracking posture shifts across frames to form discriminative cues for fall prediction. Training was conducted on multiple public datasets with diverse backgrounds and subject body types. The model was compared with logistic regression and CNN baselines trained on identical data splits. Results and Discussion. The Vision Transformer achieved 99.1% accuracy on the primary dataset and 97.9% on the UR Fall Detection Dataset, surpassing logistic regression, CNN, and LSTM baselines. It maintained higher precision and recall in indoor and outdoor scenes and reduced false alarm rates. Stable performance under rapid movement and variable lighting demonstrated robustness gains. Cross-dataset evaluation confirmed effective transfer of learned spatial-temporal representations to unseen environments. Conclusion. Vision Transformers offer an effective approach for real-time, non-invasive fall detection in clinical and home settings. Their capacity to capture spatial-temporal motion patterns through self-attention, without handcrafted features, supports broader deployment in intelligent surveillance systems. The proposed framework demonstrates strong generalization across datasets and recording conditions. Future work will target edge-device optimization and multi-modal data integration. Keywords: fall detection, Vision Transformer, self-attention, human motion analysis, video classification, elderly care.
Copyright (c) 2026 Ivan Ursul, Andriy Pereymybida http://creativecommons.org/licenses/by/4.0
THE EFFECT OF PREPARATION CONDITIONS ON THE ELECTRICAL CONDUCTIVITY OF THIN FILMS OF (Y0.06Ga0.94)2О3
Background. β-Ga2O3 gallium oxideis a promising wide-bandgap semiconductor widely used in optoelectronic and sensing applications. The electrical conductivity of thin films strongly depends on their structural quality, defect states, and post-deposition treatment conditions. In polycrystalline films, grain boundaries and defect complexes significantly affect charge transport mechanisms. The objective of this study is to investigate the influence of annealing atmospheres on the structural, morphological, and electrical properties of (Y0.06Ga0.94)2О3 thin films. Materials and Methods. Thin films of (Y0.06Ga0.94)2О3 with thicknesses of 0.3–1.0 μm were deposited by RF ion-plasma sputtering onto fused quartz substrates. Post-deposition annealing was carried out in oxygen and argon atmospheres at 1000–1100 °C, and in hydrogen at 600–650 °C. Structural properties were analyzed using X-ray diffraction, while surface morphology was examined by atomic force microscopy. Electrical conductivity was measured in the temperature range of 300–450 K, and activation energies were determined from temperature-dependent conductivity data. Results and Discussion. X-ray analysis confirmed the formation of films in the monoclinic β-Ga2O3 phase, with enhanced crystallinity and preferred orientation after annealing in oxygen. It has been established that freshly deposited films have a high resistivity (ρ > 10¹¹ Ω·cm), which decreases with increasing temperature and after annealing. Oxygen annealing resulted in activation energy of ~0.87 eV, while argon annealing produced higher values (~1.38 eV in 300–400 K range), indicating deeper donor levels associated with oxygen vacancies. Hydrogen annealing significantly reduced resistivity (~10⁸ Ω·cm) and activation energy (~0.40 eV), attributed to shallow donor states. Conclusion. The electrical conductivity of (Y0.06Ga0.94)2О3 thin films is governed by defect-related donor levels formed during annealing. Oxygen and argon atmospheres promote deep donor states, while hydrogen enhances shallow donor formation, leading to improved electrical conductivity. Keywords: thin films, gallium oxide, crystalline structure, impurities, electrical conductivity
Copyright (c) 2026 Ihor Kukharskyy, Iryna Kofliuk, Ivanna Medvid, Ihor Kuz, Oleh Bordun http://creativecommons.org/licenses/by/4.0
PHOTODETECTORS BASED ON FIELD EFFECT IN POROUS SILICON – REDUCED GRAPHENE OXIDE STRUCTURES
Background. Graphene field-effect transistors (FETs) have high potential for application in sensor electronics as detectors of electromagnetic radiation in a wide spectral range due to the high sensitivity of graphene ambipolar conductivity to local changes in the electric field. The use of a reduced graphene oxide (RGO) film provides cost reduction of photodetectors based on graphene FETs. On the other hand, an additional porous silicon light-absorbing layer can increase their sensitivity due to an increase in surface area. Materials and methods. Graphene field-effect photodetectors were created by drying a film-forming RGO suspension deposited on the surface of the porous silicon on a silicon substrate, which served as the gate of the FET. Electrical source and drain contacts were formed on the surface of the obtained RGO film. To improve the insulating properties of porous silicon, it was electrochemically oxidized, and an additional layer of Al2O3 was deposited. The electrical and photoelectric properties of the created field-effect photodetectors were investigated in DC and AC modes using a white LED and standard optical equipment. Results. An increase in the conductivity and capacitance of the RGO channel of the FETs was detected under the influence of white light irradiation. Based on the analysis of the drain current dependencies on the gate voltage, it has been established that the efficiency and photosensitivity of the FETs based on the porous silicon and RGO film are increased by the deposition of an additional Al2O3 layer on the surface of electrochemically oxidized porous silicon. The maximum sensitivity of the created photodetectors is in the spectral range of 800–900 nm. The response time to white light pulses is about 0.5 ms. Passivation of the porous silicon surface with the oxide film and the Al2O3 layer causes an increase in the photosignal relaxation time. Conclusions. The features of using FETs based on porous silicon structures and RGO film as visible radiation detectors have been investigated. The electrical, spectral, and time characteristics of the created field-effect photodetectors were determined. Keywords: Graphene field-effect transistor, reduced graphene oxide, porous silicon, photosensitivity.
Copyright (c) 2026 Igor Olenych, Andrii Kozak http://creativecommons.org/licenses/by/4.0
INTEGRATION OF DECENTRALIZED PERFORMANCE VERIFICATION IN HYBRID ARCHITECTURES EDGE-FOG-CLOUD TO INCREASE IoT SYSTEMS RELIABILITY
Background. The rapid growth of Internet of Things (IoT) systems has increased the demand for scalable and low-latency data processing architectures. Traditional cloud-centric approaches often suffer from high communication delays and bandwidth limitations. Edge–Fog–Cloud computing introduces a multi-tier model that distributes computational tasks closer to data sources. However, evaluating computational methods in such heterogeneous environments requires systematic performance analysis and architectural optimization. In this context, integrating mathematically stable and computationally efficient methods, such as harmonic potential field–based approaches, is essential to ensure reliable real-time operation, scalability, and system resilience across distributed layers. Methods. This study evaluates the Laplace artificial potential field method implemented within a multi-tier Edge–Fog–Cloud architecture. The experimental framework includes distributed simulation, real-time processing scenarios, and comparative benchmarking. Performance metrics such as latency, computational load, and system stability were analyzed. The proposed approach was tested under variable workload conditions to assess scalability and efficiency across architectural layers. Results and Discussion. Experimental results demonstrate reduced end-to-end latency and improved task distribution across edge and fog layers. Compared to centralized processing, the proposed architecture maintains stability under increased workload. The Laplace-based computational model ensures efficient obstacle handling and balanced resource utilization. These findings confirm that multi-tier orchestration enhances system responsiveness while preserving acceptable computational overhead in dynamic IoT environments. Conclusion. Integrating the Laplace artificial potential field method within an Edge–Fog–Cloud architecture significantly improves distributed system performance. The proposed framework increases scalability, reliability, and computational efficiency in real-time IoT applications, providing a solid foundation for further optimization of resource management and intelligent task allocation in heterogeneous distributed environments. Keywords: Edge–Fog–Cloud; IoT; Laplace artificial potential field; distributed computing; real-time processing; latency; runtime verification.
Copyright (c) 2026 Roman Diachok, Halyna Klym http://creativecommons.org/licenses/by/4.0